Data Sharing During Pandemics: Reciprocity, Solidarity, and Limits to Obligations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
South Africa shared with the world the warning of a new strain of SARS-CoV2, Omicron, in November 2021. As a result, many high-income countries (HICs) instituted complete travel bans on persons leaving South Africa and other neighbouring countries. These bans were unnecessary from a scientific standpoint, and they ran counter to the International Health Regulations. In short, South Africa was penalized for sharing data. Data sharing during pandemics is commonly justified by appeals to solidarity. In this paper, we argue that solidarity is, at best, an aspirational ideal to work toward but that it cannot ground an obligation to share data. Instead, low-and-middle income countries (LIMCs) should be guided by the principle of reciprocity, which states that we ought to return good for good received. Reciprocity is necessarily a conditional principle. LMICs, we argue, should only share data during future pandemics on the condition that HICs provide enforceable assurances that the benefits of data sharing will be equitably distributed and that LMICs won't be penalized for sharing information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it